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						<h3 class="w3ls_pvt-title" style="width: 560px;margin-right: 40px;">机器学习算法原理<span>可视化</span>平台</h3>
						<p class="text-sty-banner">机器学习算法原理可视化的具体需求和主要发展方向:<br />
							对机器学习进行更加透彻的研究和学习<br />深度理解和分析机器学习算法的根本原理<br />
							将其核心原理以可视化的方式进行重塑和构建<br />搭建平台对机器学习的算法原理进行可视化的展现</p>
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						<h4 class="my-4">线性回归</h4>
						<p>线性回归是利用数理统计中回归分析，来确定两种或两种以上变量间相互依赖的定量关系的一种统计分析方法，运用十分广泛。其表达形式为y = w'x+e，e为误差服从均值为0的正态分布。</p>
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						<h4 class="my-4">K近邻算法</h4>
						<p>K最近邻(KNN)分类算法，是一个理论上比较成熟的方法。该方法的思路是：在特征空间中，如果一个样本附近的k个最近样本的大多数属于某一个类别，则该样本也属于这个类别。</p>
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						<p>决策树(Decision Tree)是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。</p>
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					<h3 class="w3ls_pvt-title"><span>平台简介</span></h3>
					<p class="text-sty-banner">&emsp;&emsp;这是一个机器学习算法的学习平台或辅助学习平台，希望通过图形等可视化方法来展现算法的原理，帮助学习者理解。</p>
					<p class="text-sty-banner">&emsp;&emsp;本平台采用了典型例题分析的方式进行可视化展示，提供了多种输入方式，并以图形化文字化等输出方式进行展示。</p>
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